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Sepsis is a vital issue in critical care medicine, and early detection and intervention are key to survival. We aimed to establish an early warning system for sepsis based on a data integration platform that can be implemented in the ICU.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| non-sepsis |
| ||
| sepsis |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| no intervention | Other | no intervention |
|
| Measure | Description | Time Frame |
|---|---|---|
| confidence index of sepsis occurrence | sepsis occurrence in five hours |
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Inclusion Criteria:
Patients who met all of the following criteria were included in the case group:
Exclusion Criteria:
Patients meeting all of the following criteria were included in the control group:
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patients that were admitted into the ICU.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Ranran Li, PhD | Contact | 18317059746 | rebecca1009@hotmail.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| ICU, Ruijin Hospital | Recruiting | Shanghai | Shanghai Municipality | 200025 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36581881 | Derived | Chen Q, Li R, Lin C, Lai C, Chen D, Qu H, Huang Y, Lu W, Tang Y, Li L. Transferability and interpretability of the sepsis prediction models in the intensive care unit. BMC Med Inform Decis Mak. 2022 Dec 29;22(1):343. doi: 10.1186/s12911-022-02090-3. |
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